Artificial intelligence water purifier

ABSTRACT

Disclosed is an artificial intelligence (AI) water purifier. The AI water purifier includes a housing forming an outer surface of the AI water purifier, a filter assembly disposed inside the housing, a water outlet for discharging water, a water supply pipe for connecting a water source to the filter assembly, a water discharge pipe for connecting the filter assembly to the water outlet, a first camera for capturing an image of water passing through the water supply pipe, and a processor for acquiring at least one of transparency or color of the water passing through the water supply pipe using the image captured, and for determining a pollution level of the water passing through the water supply pipe using at least one of the transparency or the color.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. 119 and 365 to KoreanPatent Application No. 10-2019-0159507, filed on Dec. 4, 2019 in theKorean Intellectual Property Office, the disclosure of which isincorporated herein by reference.

FIELD OF INVENTION

The present disclosure relates to a water purifier for photographingwater passing a water supply pipe and recognizing a pollution level ofwater using an image captured by photographing water.

BACKGROUND

Artificial intelligence refers to one field of computer engineering andinformation technology of studying a method for making a computer think,learn, and do self-improvement, which is achieved based on humanintelligence, and means that a computer emulates an intelligent behaviorof the human.

AI is largely related directly and indirectly to other fields of acomputer science rather than existing itself. In particular, AI elementshave been modernly introduced in various fields of informationtechnology, and there has been an active attempt to use AI to overcomeproblems of the fields.

Research has been actively conducted into context awareness technologyof recognizing a situation of a user and providing information desiredby the user in a desired form using AI.

In addition, an electronic device for providing various operations andfunctions can be referred to as an AI device.

A water purifier is a device for purifying impurities contained in wateror harmful materials such as heavy metal using a physical and/orchemical method.

A currently available water purifier operates without checking waterquality. Thus, when water supply is suddenly contaminated due to rust orthe like, the lifespan of a filter is largely reduced, and unpurifiedwater is discharged.

SUMMARY OF THE INVENTION

An object of the present disclosure is to provide an artificialintelligence (AI) water purifier for photographing water passing a watersupply pipe and recognizing a pollution level of water using an imagecaptured by photographing water.

According to an embodiment, an artificial intelligence (AI) waterpurifier includes a housing forming an outer surface of the AI waterpurifier, a filter assembly disposed inside the housing, a water outletconfigured to discharge water, a water supply pipe configured to connecta water source and the filter assembly, a water discharge pipeconfigured to connect the filter assembly and the water outlet, a firstcamera configured to photograph water passing through the water supplypipe, and a processor configured to acquire at least one of transparencyor color of the water passing through the water supply pipe using animage captured by photographing the water passing through the watersupply pipe, and to determine a pollution level of the water passingthrough the water supply pipe using at least one of the transparency orthe color.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an artificial intelligence (AI) device according toan embodiment of the present disclosure.

FIG. 2 illustrates an AI server according to an embodiment of thepresent disclosure.

FIG. 3 illustrates an AI system according to an embodiment of thepresent disclosure;

FIG. 4 is a front perspective view of a water purifier according to anembodiment of the present disclosure;

FIG. 5 is a system diagram illustrating a water flow channel connectedto a water purifier according to an embodiment of the presentdisclosure;

FIG. 6 is a view for explaining an operation method of an AI waterpurifier;

FIG. 7 is a view for explaining a water supply pipe and a supplied waterquality management module;

FIG. 8 is a plan view of a photography surface and a photography areaformed on an inner surface of a water supply pipe;

FIGS. 9 to 11 are views for explaining a method of determining apollution level using an image captured by photographing water;

FIG. 12 is a view for explaining a method of giving a warning orstopping an operation depending on a pollution level of water passingthrough a water supply pipe;

FIG. 13 is a view for explaining a method of determining a pollutionsource; and

FIG. 14 is a view for explaining a method of stopping water discharge ina water discharge pipe or outputting notification of discharged watercheck.

DETAILED DESCRIPTION

Hereinafter, embodiments of the present disclosure are described in moredetail with reference to accompanying drawings and regardless of thedrawings symbols, same or similar components are assigned with the samereference numerals and thus overlapping descriptions for those areomitted. The suffixes “module” and “unit” for components used in thedescription below are assigned or mixed in consideration of easiness inwriting the specification and do not have distinctive meanings or rolesby themselves. In the following description, detailed descriptions ofwell-known functions or constructions will be omitted since they wouldobscure the disclosure in unnecessary detail. Additionally, theaccompanying drawings are used to help easily understanding embodimentsdisclosed herein but the technical idea of the present disclosure is notlimited thereto. It should be understood that all of variations,equivalents or substitutes contained in the concept and technical scopeof the present disclosure are also included.

It will be understood that the terms “first” and “second” are usedherein to describe various components but these components should not belimited by these terms. These terms are used only to distinguish onecomponent from other components.

In this disclosure below, when one part (or element, device, etc.) isreferred to as being ‘connected’ to another part (or element, device,etc.), it should be understood that the former can be ‘directlyconnected’ to the latter, or ‘electrically connected’ to the latter viaan intervening part (or element, device, etc.). It will be furtherunderstood that when one component is referred to as being ‘directlyconnected’ or ‘directly linked’ to another component, it means that nointervening component is present.

Artificial Intelligence (AI)

Artificial intelligence refers to the field of studying artificialintelligence or methodology for making artificial intelligence, andmachine learning refers to the field of defining various issues dealtwith in the field of artificial intelligence and studying methodologyfor solving the various issues. Machine learning is defined as analgorithm that enhances the performance of a certain task through asteady experience with the certain task.

An artificial neural network (ANN) is a model used in machine learningand can mean a whole model of problem-solving ability which is composedof artificial neurons (nodes) that form a network by synapticconnections. The artificial neural network can be defined by aconnection pattern between neurons in different layers, a learningprocess for updating model parameters, and an activation function forgenerating an output value.

The artificial neural network can include an input layer, an outputlayer, and optionally one or more hidden layers. Each layer includes oneor more neurons, and the artificial neural network can include a synapsethat links neurons to neurons. In the artificial neural network, eachneuron can output the function value of the activation function forinput signals, weights, and deflections input through the synapse.

Model parameters refer to parameters determined through learning andinclude a weight value of synaptic connection and deflection of neurons.A hyperparameter means a parameter to be set in the machine learningalgorithm before learning, and includes a learning rate, a repetitionnumber, a mini batch size, and an initialization function.

The purpose of the learning of the artificial neural network can be todetermine the model parameters that minimize a loss function. The lossfunction can be used as an index to determine optimal model parametersin the learning process of the artificial neural network.

Machine learning can be classified into supervised learning,unsupervised learning, and reinforcement learning according to alearning method.

The supervised learning can refer to a method of learning an artificialneural network in a state in which a label for learning data is given,and the label can mean the correct answer (or result value) that theartificial neural network must infer when the learning data is input tothe artificial neural network. The unsupervised learning can refer to amethod of learning an artificial neural network in a state in which alabel for learning data is not given. The reinforcement learning canrefer to a learning method in which an agent defined in a certainenvironment learns to select a behavior or a behavior sequence thatmaximizes cumulative compensation in each state.

Machine learning, which is implemented as a deep neural network (DNN)including a plurality of hidden layers among artificial neural networks,is also referred to as deep learning, and the deep running is part ofmachine running. In the following, machine learning is used to mean deeprunning.

Robot

A robot can refer to a machine that automatically processes or operatesa given task by its own ability. In particular, a robot having afunction of recognizing an environment and performing aself-determination operation can be referred to as an intelligent robot.

Robots can be classified into industrial robots, medical robots, homerobots, military robots, and the like according to the use purpose orfield.

The robot includes a driving unit can include an actuator or a motor andcan perform various physical operations such as moving a robot joint. Inaddition, a movable robot can include a wheel, a brake, a propeller, andthe like in a driving unit, and can travel on the ground through thedriving unit or fly in the air.

Self-Driving

Self-driving refers to a technique of driving for oneself, and aself-driving vehicle refers to a vehicle that travels without anoperation of a user or with a minimum operation of a user.

For example, the self-driving can include a technology for maintaining alane while driving, a technology for automatically adjusting a speed,such as adaptive cruise control, a technique for automatically travelingalong a predetermined route, and a technology for automatically settingand traveling a route when a destination is set.

The vehicle can include a vehicle having only an internal combustionengine, a hybrid vehicle having an internal combustion engine and anelectric motor together, and an electric vehicle having only an electricmotor, and can include not only an automobile but also a train, amotorcycle, and the like.

At this time, the self-driving vehicle can be regarded as a robot havinga self-driving function.

eXtended Reality (XR)

Extended reality is collectively referred to as virtual reality (VR),augmented reality (AR), and mixed reality (MR). The VR technologyprovides a real-world object and background only as a CG image, the ARtechnology provides a virtual CG image on a real object image, and theMR technology is a computer graphic technology that mixes and combinesvirtual objects into the real world.

The MR technology is similar to the AR technology in that the realobject and the virtual object are shown together. However, in the ARtechnology, the virtual object is used in the form that complements thereal object, whereas in the MR technology, the virtual object and thereal object are used in an equal manner.

The XR technology can be applied to a head-mount display (HMD), ahead-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop,a TV, a digital signage, and the like. A device to which the XRtechnology is applied can be referred to as an XR device.

FIG. 1 illustrates an AI device 100 according to an embodiment of thepresent disclosure.

The AI device 100 can be implemented by a stationary device or a mobiledevice, such as a TV, a projector, a mobile phone, a smartphone, adesktop computer, a notebook, a digital broadcasting terminal, apersonal digital assistant (PDA), a portable multimedia player (PMP), anavigation device, a tablet PC, a wearable device, a set-top box (STB),a DMB receiver, a radio, a washing machine, a refrigerator, a desktopcomputer, a digital signage, a robot, a vehicle, and the like.

Referring to FIG. 1, the AI device 100 can include a transceiver 110, aninput interface 120, a learning processor 130, a sensor 140, an outputinterface 150, a memory 170, and a processor 180.

The transceiver 110 can transmit and receive data to and from externaldevices such as other AI devices 100 a to 100 e and the AI server 200 byusing wire/wireless communication technology. For example, thetransceiver 110 can transmit and receive sensor information, a userinput, a learning model, and a control signal to and from externaldevices.

The communication technology used by the transceiver 110 includes GSM(Global System for Mobile communication), CDMA (Code Division MultiAccess), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi(Wireless-Fidelity), Bluetooth™, RFID (Radio Frequency Identification),Infrared Data Association (IrDA), ZigBee, NFC (Near FieldCommunication), and the like.

The input interface 120 can acquire various kinds of data.

At this time, the input interface 120 can include a camera for inputtinga video signal, a microphone for receiving an audio signal, and a userinput interface for receiving information from a user. The camera or themicrophone can be treated as a sensor, and the signal acquired from thecamera or the microphone can be referred to as sensing data or sensorinformation.

The input interface 120 can acquire a learning data for model learningand an input data to be used when an output is acquired by usinglearning model. The input interface 120 can acquire raw input data. Inthis case, the processor 180 or the learning processor 130 can extractan input feature by preprocessing the input data.

The learning processor 130 can learn a model composed of an artificialneural network by using learning data. The learned artificial neuralnetwork can be referred to as a learning model. The learning model canbe used to an infer result value for new input data rather than learningdata, and the inferred value can be used as a basis for determination toperform a certain operation.

At this time, the learning processor 130 can perform AI processingtogether with the learning processor 240 of the AI server 200.

At this time, the learning processor 130 can include a memory integratedor implemented in the AI device 100. Alternatively, the learningprocessor 130 can be implemented by using the memory 170, an externalmemory directly connected to the AI device 100, or a memory held in anexternal device.

The sensor 140 can acquire at least one of internal information aboutthe AI device 100, ambient environment information about the AI device100, and user information by using various sensors.

Examples of the sensors included in the sensor 140 can include aproximity sensor, an illuminance sensor, an acceleration sensor, amagnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IRsensor, a fingerprint recognition sensor, an ultrasonic sensor, anoptical sensor, a microphone, a lidar, and a radar.

The output interface 150 can generate an output related to a visualsense, an auditory sense, or a haptic sense.

At this time, the output interface 150 can include a display unit foroutputting time information, a speaker for outputting auditoryinformation, and a haptic module for outputting haptic information.

The memory 170 can store data that supports various functions of the AIdevice 100. For example, the memory 170 can store input data acquired bythe input interface 120, learning data, a learning model, a learninghistory, and the like.

The processor 180 can determine at least one executable operation of theAI device 100 based on information determined or generated by using adata analysis algorithm or a machine learning algorithm. The processor180 can control the components of the AI device 100 to execute thedetermined operation.

To this end, the processor 180 can request, search, receive, or utilizedata of the learning processor 130 or the memory 170. The processor 180can control the components of the AI device 100 to execute the predictedoperation or the operation determined to be desirable among the at leastone executable operation.

When the connection of an external device is required to perform thedetermined operation, the processor 180 can generate a control signalfor controlling the external device and can transmit the generatedcontrol signal to the external device.

The processor 180 can acquire intention information for the user inputand can determine the user's requirements based on the acquiredintention information.

The processor 180 can acquire the intention information corresponding tothe user input by using at least one of a speech to text (STT) enginefor converting speech input into a text string or a natural languageprocessing (NLP) engine for acquiring intention information of a naturallanguage.

At least one of the STT engine or the NLP engine can be configured as anartificial neural network, at least part of which is learned accordingto the machine learning algorithm. At least one of the STT engine or theNLP engine can be learned by the learning processor 130, can be learnedby the learning processor 240 of the AI server 200, or can be learned bytheir distributed processing.

The processor 180 can collect history information including theoperation contents of the AI apparatus 100 or the user's feedback on theoperation and can store the collected history information in the memory170 or the learning processor 130 or transmit the collected historyinformation to the external device such as the AI server 200. Thecollected history information can be used to update the learning model.

The processor 180 can control at least part of the components of AIdevice 100 so as to drive an application program stored in memory 170.Furthermore, the processor 180 can operate two or more of the componentsincluded in the AI device 100 in combination so as to drive theapplication program.

FIG. 2 illustrates an AI server 200 according to an embodiment of thepresent disclosure.

Referring to FIG. 2, the AI server 200 can refer to a device that learnsan artificial neural network by using a machine learning algorithm oruses a learned artificial neural network. The AI server 200 can includea plurality of servers to perform distributed processing, or can bedefined as a 5G network. At this time, the AI server 200 can be includedas a partial configuration of the AI device 100, and can perform atleast part of the AI processing together.

The AI server 200 can include a transceiver 210, a memory 230, alearning processor 240, a processor 260, and the like.

The transceiver 210 can transmit and receive data to and from anexternal device such as the AI device 100.

The memory 230 can include a model storage unit 231. The model storageunit 231 can store a learning or learned model (or an artificial neuralnetwork 231 a) through the learning processor 240.

The learning processor 240 can learn the artificial neural network 231 aby using the learning data. The learning model can be used in a state ofbeing mounted on the AI server 200 of the artificial neural network, orcan be used in a state of being mounted on an external device such asthe AI device 100.

The learning model can be implemented in hardware, software, or acombination of hardware and software. If all or some of the learningmodels are implemented in software, one or more instructions thatconstitute the learning model can be stored in memory 230.

The processor 260 can infer the result value for new input data by usingthe learning model and can generate a response or a control commandbased on the inferred result value.

FIG. 3 illustrates an AI system 1 according to an embodiment of thepresent disclosure.

Referring to FIG. 3, in the AI system 1, at least one of an AI server200, a robot 100 a, a self-driving vehicle 100 b, an XR device 100 c, asmartphone 100 d, or a home appliance 100 e is connected to a cloudnetwork 10. The robot 100 a, the self-driving vehicle 100 b, the XRdevice 100 c, the smartphone 100 d, or the home appliance 100 e, towhich the AI technology is applied, can be referred to as AI devices 100a to 100 e.

The cloud network 10 can refer to a network that forms part of a cloudcomputing infrastructure or exists in a cloud computing infrastructure.The cloud network 10 can be configured by using a 3G network, a 4G orLTE network, or a 5G network.

That is, the devices 100 a to 100 e and 200 configuring the AI system 1can be connected to each other through the cloud network 10. Inparticular, each of the devices 100 a to 100 e and 200 can communicatewith each other through a base station, but can directly communicatewith each other without using a base station.

The AI server 200 can include a server that performs AI processing and aserver that performs operations on big data.

The AI server 200 can be connected to at least one of the AI devicesconstituting the AI system 1, that is, the robot 100 a, the self-drivingvehicle 100 b, the XR device 100 c, the smartphone 100 d, or the homeappliance 100 e through the cloud network 10, and can assist at leastpart of AI processing of the connected AI devices 100 a to 100 e.

At this time, the AI server 200 can learn the artificial neural networkaccording to the machine learning algorithm instead of the AI devices100 a to 100 e, and can directly store the learning model or transmitthe learning model to the AI devices 100 a to 100 e.

At this time, the AI server 200 can receive input data from the AIdevices 100 a to 100 e, can infer the result value for the receivedinput data by using the learning model, can generate a response or acontrol command based on the inferred result value, and can transmit theresponse or the control command to the AI devices 100 a to 100 e.

Alternatively, the AI devices 100 a to 100 e can infer the result valuefor the input data by directly using the learning model, and cangenerate the response or the control command based on the inferenceresult.

Hereinafter, various embodiments of the AI devices 100 a to 100 e towhich the above-described technology is applied will be described. TheAI devices 100 a to 100 e illustrated in FIG. 3 can be regarded as aspecific embodiment of the AI device 100 illustrated in FIG. 1.

AI+Robot

The robot 100 a, to which the AI technology is applied, can beimplemented as a guide robot, a carrying robot, a cleaning robot, awearable robot, an entertainment robot, a pet robot, an unmanned flyingrobot, or the like.

The robot 100 a can include a robot control module for controlling theoperation, and the robot control module can refer to a software moduleor a chip implementing the software module by hardware.

The robot 100 a can acquire state information about the robot 100 a byusing sensor information acquired from various kinds of sensors, candetect (recognize) surrounding environment and objects, can generate mapdata, can determine the route and the travel plan, can determine theresponse to user interaction, or can determine the operation.

The robot 100 a can use the sensor information acquired from at leastone sensor among the lidar, the radar, and the camera so as to determinethe travel route and the travel plan.

The robot 100 a can perform the above-described operations by using thelearning model composed of at least one artificial neural network. Forexample, the robot 100 a can recognize the surrounding environment andthe objects by using the learning model, and can determine the operationby using the recognized surrounding information or object information.The learning model can be learned directly from the robot 100 a or canbe learned from an external device such as the AI server 200.

At this time, the robot 100 a can perform the operation by generatingthe result by directly using the learning model, but the sensorinformation can be transmitted to the external device such as the AIserver 200 and the generated result can be received to perform theoperation.

The robot 100 a can use at least one of the map data, the objectinformation detected from the sensor information, or the objectinformation acquired from the external apparatus to determine the travelroute and the travel plan, and can control the driving unit such thatthe robot 100 a travels along the determined travel route and travelplan.

The map data can include object identification information about variousobjects arranged in the space in which the robot 100 a moves. Forexample, the map data can include object identification informationabout fixed objects such as walls and doors and movable objects such aspollen and desks. The object identification information can include aname, a type, a distance, and a position.

In addition, the robot 100 a can perform the operation or travel bycontrolling the driving unit based on the control/interaction of theuser. At this time, the robot 100 a can acquire the intentioninformation of the interaction due to the user's operation or speechutterance, and can determine the response based on the acquiredintention information, and can perform the operation.

AI+Self-Driving

The self-driving vehicle 100 b, to which the AI technology is applied,can be implemented as a mobile robot, a vehicle, an unmanned flyingvehicle, or the like.

The self-driving vehicle 100 b can include a self-driving control modulefor controlling a self-driving function, and the self-driving controlmodule can refer to a software module or a chip implementing thesoftware module by hardware. The self-driving control module can beincluded in the self-driving vehicle 100 b as a component thereof, butcan be implemented with separate hardware and connected to the outsideof the self-driving vehicle 100 b.

The self-driving vehicle 100 b can acquire state information about theself-driving vehicle 100 b by using sensor information acquired fromvarious kinds of sensors, can detect (recognize) surrounding environmentand objects, can generate map data, can determine the route and thetravel plan, or can determine the operation.

Like the robot 100 a, the self-driving vehicle 100 b can use the sensorinformation acquired from at least one sensor among the lidar, theradar, and the camera so as to determine the travel route and the travelplan.

In particular, the self-driving vehicle 100 b can recognize theenvironment or objects for an area covered by a field of view or an areaover a certain distance by receiving the sensor information fromexternal devices, or can receive directly recognized information fromthe external devices.

The self-driving vehicle 100 b can perform the above-describedoperations by using the learning model composed of at least oneartificial neural network. For example, the self-driving vehicle 100 bcan recognize the surrounding environment and the objects by using thelearning model, and can determine the traveling movement line by usingthe recognized surrounding information or object information. Thelearning model can be learned directly from the self-driving vehicle 100a or can be learned from an external device such as the AI server 200.

At this time, the self-driving vehicle 100 b can perform the operationby generating the result by directly using the learning model, but thesensor information can be transmitted to the external device such as theAI server 200 and the generated result can be received to perform theoperation.

The self-driving vehicle 100 b can use at least one of the map data, theobject information detected from the sensor information, or the objectinformation acquired from the external apparatus to determine the travelroute and the travel plan, and can control the driving unit such thatthe self-driving vehicle 100 b travels along the determined travel routeand travel plan.

The map data can include object identification information about variousobjects arranged in the space (for example, road) in which theself-driving vehicle 100 b travels. For example, the map data caninclude object identification information about fixed objects such asstreet lamps, rocks, and buildings and movable objects such as vehiclesand pedestrians. The object identification information can include aname, a type, a distance, and a position.

In addition, the self-driving vehicle 100 b can perform the operation ortravel by controlling the driving unit based on the control/interactionof the user. At this time, the self-driving vehicle 100 b can acquirethe intention information of the interaction due to the user's operationor speech utterance, and can determine the response based on theacquired intention information, and can perform the operation.

AI+XR

The XR device 100 c, to which the AI technology is applied, can beimplemented by a head-mount display (HMD), a head-up display (HUD)provided in the vehicle, a television, a mobile phone, a smartphone, acomputer, a wearable device, a home appliance, a digital signage, avehicle, a fixed robot, a mobile robot, or the like.

The XR device 100 c can analyzes three-dimensional point cloud data orimage data acquired from various sensors or the external devices,generate position data and attribute data for the three-dimensionalpoints, acquire information about the surrounding space or the realobject, and render to output the XR object to be output. For example,the XR device 100 c can output an XR object including the additionalinformation about the recognized object in correspondence to therecognized object.

The XR device 100 c can perform the above-described operations by usingthe learning model composed of at least one artificial neural network.For example, the XR device 100 c can recognize the real object from thethree-dimensional point cloud data or the image data by using thelearning model, and can provide information corresponding to therecognized real object. The learning model can be directly learned fromthe XR device 100 c, or can be learned from the external device such asthe AI server 200.

At this time, the XR device 100 c can perform the operation bygenerating the result by directly using the learning model, but thesensor information can be transmitted to the external device such as theAI server 200 and the generated result can be received to perform theoperation.

AI+Robot+Self-Driving

The robot 100 a, to which the AI technology and the self-drivingtechnology are applied, can be implemented as a guide robot, a carryingrobot, a cleaning robot, a wearable robot, an entertainment robot, a petrobot, an unmanned flying robot, or the like.

The robot 100 a, to which the AI technology and the self-drivingtechnology are applied, can refer to the robot itself having theself-driving function or the robot 100 a interacting with theself-driving vehicle 100 b.

The robot 100 a having the self-driving function can collectively referto a device that moves for itself along the given movement line withoutthe user's control or moves for itself by determining the movement lineby itself.

The robot 100 a and the self-driving vehicle 100 b having theself-driving function can use a common sensing method so as to determineat least one of the travel route or the travel plan. For example, therobot 100 a and the self-driving vehicle 100 b having the self-drivingfunction can determine at least one of the travel route or the travelplan by using the information sensed through the lidar, the radar, andthe camera.

The robot 100 a that interacts with the self-driving vehicle 100 bexists separately from the self-driving vehicle 100 b and can performoperations interworking with the self-driving function of theself-driving vehicle 100 b or interworking with the user who rides onthe self-driving vehicle 100 b.

At this time, the robot 100 a interacting with the self-driving vehicle100 b can control or assist the self-driving function of theself-driving vehicle 100 b by acquiring sensor information on behalf ofthe self-driving vehicle 100 b and providing the sensor information tothe self-driving vehicle 100 b, or by acquiring sensor information,generating environment information or object information, and providingthe information to the self-driving vehicle 100 b.

Alternatively, the robot 100 a interacting with the self-driving vehicle100 b can monitor the user boarding the self-driving vehicle 100 b, orcan control the function of the self-driving vehicle 100 b through theinteraction with the user. For example, when it is determined that thedriver is in a drowsy state, the robot 100 a can activate theself-driving function of the self-driving vehicle 100 b or assist thecontrol of the driving unit of the self-driving vehicle 100 b. Thefunction of the self-driving vehicle 100 b controlled by the robot 100 acan include not only the self-driving function but also the functionprovided by the navigation system or the audio system provided in theself-driving vehicle 100 b.

Alternatively, the robot 100 a that interacts with the self-drivingvehicle 100 b can provide information or assist the function to theself-driving vehicle 100 b outside the self-driving vehicle 100 b. Forexample, the robot 100 a can provide traffic information includingsignal information and the like, such as a smart signal, to theself-driving vehicle 100 b, and automatically connect an electriccharger to a charging port by interacting with the self-driving vehicle100 b like an automatic electric charger of an electric vehicle.

AI+Robot+XR

The robot 100 a, to which the AI technology and the XR technology areapplied, can be implemented as a guide robot, a carrying robot, acleaning robot, a wearable robot, an entertainment robot, a pet robot,an unmanned flying robot, a drone, or the like.

The robot 100 a, to which the XR technology is applied, can refer to arobot that is subjected to control/interaction in an XR image. In thiscase, the robot 100 a can be separated from the XR device 100 c andinterwork with each other.

When the robot 100 a, which is subjected to control/interaction in theXR image, can acquire the sensor information from the sensors includingthe camera, the robot 100 a or the XR device 100 c can generate the XRimage based on the sensor information, and the XR device 100 c canoutput the generated XR image. The robot 100 a can operate based on thecontrol signal input through the XR device 100 c or the user'sinteraction.

For example, the user can confirm the XR image corresponding to the timepoint of the robot 100 a interworking remotely through the externaldevice such as the XR device 100 c, adjust the self-driving travel pathof the robot 100 a through interaction, control the operation ordriving, or confirm the information about the surrounding object.

AI+Self-Driving+XR

The self-driving vehicle 100 b, to which the AI technology and the XRtechnology are applied, can be implemented as a mobile robot, a vehicle,an unmanned flying vehicle, or the like.

The self-driving driving vehicle 100 b, to which the XR technology isapplied, can refer to a self-driving vehicle having a means forproviding an XR image or a self-driving vehicle that is subjected tocontrol/interaction in an XR image. Particularly, the self-drivingvehicle 100 b that is subjected to control/interaction in the XR imagecan be distinguished from the XR device 100 c and interwork with eachother.

The self-driving vehicle 100 b having the means for providing the XRimage can acquire the sensor information from the sensors including thecamera and output the generated XR image based on the acquired sensorinformation. For example, the self-driving vehicle 100 b can include anHUD to output an XR image, thereby providing a passenger with a realobject or an XR object corresponding to an object in the screen.

At this time, when the XR object is output to the HUD, at least part ofthe XR object can be outputted so as to overlap the actual object towhich the passenger's gaze is directed. Meanwhile, when the XR object isoutput to the display provided in the self-driving vehicle 100 b, atleast part of the XR object can be output so as to overlap the object inthe screen. For example, the self-driving vehicle 100 b can output XRobjects corresponding to objects such as a lane, another vehicle, atraffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, abuilding, and the like.

When the self-driving vehicle 100 b, which is subjected tocontrol/interaction in the XR image, can acquire the sensor informationfrom the sensors including the camera, the self-driving vehicle 100 b orthe XR device 100 c can generate the XR image based on the sensorinformation, and the XR device 100 c can output the generated XR image.The self-driving vehicle 100 b can operate based on the control signalinput through the external device such as the XR device 100 c or theuser's interaction.

A water purifier is a device for purifying impurities contained in wateror harmful materials such as heavy metal using a physical and/orchemical method.

FIG. 4 is a front perspective view of a water purifier according to anembodiment of the present disclosure.

Referring to FIG. 4, a water purifier 10 can be a direct water-type coldand warm water purifier for cooling or heating water that is directlysupplied from an external water source and ejecting the water.

In detail, the water purifier 10 can include a base 11 that forms abottom portion, a housing 12 disposed on an edge of an upper surface ofthe base 11, a cover 13 that covers an open upper surface of the housing12, a control panel 14 that formed on the upper surface of the cover 13,and a water chute 15 that protrudes from an outer circumference of thehousing 12.

In more detail, a portion 16 on which the water chute 15 is formed canbe defined as a front surface of the water purifier 10, such as a frontsurface of the housing 12 and an opposite surface thereto can be definedas a rear surface. A discharge grill can be formed at a lower end of therear surface of the housing 12, and thus, air that exchanges heat with acondenser (which will be described below) installed inside the housingcan be discharged out of the housing 12.

The housing 12 can form an outer surface of the water purifier.

The control panel 14 can include a panel body 141, and a panel cover 142that cover an upper surface of the panel body 141. Holes or grooves forinstalling a plurality of buttons can be formed in the panel body 141,and the buttons can be installed in the holes or the grooves. Buttonmenus corresponding to the buttons can be printed on the panel cover142.

The water chute 15 can extend forward from the front surface of thehousing 12 by a predetermined length, and can be installed to berotatable by 90 degrees in left and right directions based on the centerof a front end of the water purifier 10. That is, the water chute 15 canbe rotatable by 180 degrees.

A water outlet 151 for ejecting water can be formed on a bottom surfaceof the water chute 15. The water outlet 151 can be formed in a single orplural number, and when a single water outlet 151 is present, a flowchannel can be formed to discharge cold water, filtered water, and hotwater through a single water outlet.

A sensor 152 can be installed on the bottom surface of the water chute15, and thus when a user positions a storage container such as a cupbelow the water chute 15, water can be ejected.

A plurality of components such as a cold water generator for generatingcold water and a refrigeration cycle for cooling can be accommodatedinside the housing 12 that forms an outer appearance of the waterpurifier 10.

In detail, the water purifier 10 can include a compressor forcompressing a refrigerant to a vapor-phase refrigerant at a hightemperature and a high pressure, a condenser disposed behind the base 11and configured to condense the refrigerant discharged from thecompressor to a liquid-phase refrigerant at a high temperature and ahigh temperature, and a condenser fan configured to intake air of anindoor space with the water purifier 10 positioned therein and toexchange heat with the condenser.

The water purifier 10 can further include a filter assembly 17 forfiltering impurities contained in water supplied from the water source.The filter assembly 17 can be disposed at a front end of the base. Thefilter assembly 17 can include any one or both of a pre carbon filterand an ultrafiltration filter.

The filter assembly can be installed inside the housing.

The water purifier 10 can further include an expansion side forexpanding the refrigerant discharged from the condenser to a two-phaserefrigerant at a low temperature and a low pressure, and an evaporatorin which a two-phase refrigerant at a low temperature and a lowpressure, passing through the expansion side, flows.

In detail, the water purifier 10 can further include a cold water pipe(which will be described below) in which cold water flows and a coldwater generator for accommodating the evaporator therein.

The water purifier 10 can further include a hot water heater for heatingsupplied water at a setting temperature.

FIG. 5 is a system diagram illustrating a water flow channel connectedto a water purifier according to an embodiment of the presentdisclosure.

Referring to FIG. 5, a water supply line L can be formed from a watersource S to the water chute 15 of the water purifier 10, and variousvalves and water purification components can be connected to the watersupply line L.

In detail, the water supply line L can be connected to the water sourceS, for example, a water tap in a home, and the filter assembly 17 can bedisposed at any point of the water supply line L to filter impuritiescontained in drinking water supplied from the water source S.

A flow sensor 70 can be disposed at the water supply line L connected toan outlet end of the filter assembly 17. Thus, a water supply valve 61can be controlled to be closed when a supply rate detected by the flowsensor 70 reaches a setting flow rate.

A hot water supply line L1, a cold water supply line L2, and a coldwater supply line L3 can be branched at any point of the water supplyline L that extends from the outlet end of the flow sensor 70.

A filtered water ejection valve 66 can be installed at an end of thewater supply line L that extends from the outlet end of the flow sensor70, and a hot water ejection valve 64 can be installed at an end of thehot water supply line L1.

A cold water ejection valve 65 can be installed at an end of the coldwater supply line L3, and a cold water valve 63 can be installed at anypoint of the cold water supply line L2.

In detail, the cold water valve 63 can be installed at any point of thecold water supply line L2 to adjust the amount of cold water supplied toa cold water tank 33.

A water supply line that extends from an outlet end of the hot waterejection valve 64, the cold water ejection valve 65, and the filteredwater ejection valve 66 can be connected to the water chute 15. As shownin the drawings, the water supply line can be configured to connectfiltered water, cold water, and hot water to a single water outlet orcan be configured to connect them to independent water outlets,respectively.

Although a water discharge valve 32 is illustrated to be installed on awater supply line that extends out of a cold water generator 30 in thedrawings, the actual water discharge valve 32 can be inserted into aninsulation case 31 to penetrate the same, as described with reference toFIG. 5.

A flow rate adjusting valve 62 can be installed at any point of the hotwater supply line L1, and a hot water heater 22 can be connected to thehot water supply line L1 that extends at an outlet end of the flow rateadjusting valve 62. The hot water ejection valve 64 can be installed atany point of the hot water supply line L that extends at the outlet endof the hot water heater 22.

When supplied water flows along the hot water supply line L1 and passesthrough the hot water heater 22, the water can be heated at a settingtemperature, and when a hot water selection button is pushed to open thehot water ejection valve 64, hot water can be ejected.

The hot water supply line L1, the cold water supply line L2, and thecold water supply line L3 can be recombined to a single line. Anejection valve 67 can be installed on the single line.

When the ejection valve 67 is opened under control of the processor,water (which is filtered water obtained by filtering source water) canbe discharged to the outside through the water outlet.

When the water supply valve 61 is opened under control of the processor,water (source water) can be supplied to the filter assembly from thewater source.

The processor described below can refer to an element for controlling anoperation of the water purifier 10 according to an embodiment of thepresent disclosure. The processor can receive a detection signaltransmitted from various sensors such as a temperature sensor, canreceive a command signal such as a cold water ejection command, and cangenerate and transmit a new command based on the received pieces ofinformation.

The water supply line L can include a water supply pipe for connectingthe water source S and the filter assembly 17. The water supply valve 61can be installed at a water supply pipe. When the water supply valve 61is opened under control of the processor, water (source water) can besupplied to the filter assembly from the water source.

The water supply line L can include a water discharge pipe forconnecting the filter assembly 17 and the water outlet 151. For example,the water discharge pipe can include the hot water supply line L1, thecold water supply line L2, and the cold water supply line L3.

The ejection valve 67 can be installed at the water discharge pipe. Whenthe ejection valve 67 is opened under control of the processor, water(which is filtered water generated by filtering source water) can bedischarged to the outside through the water outlet.

The water purifier can include all or some of the components of the AIdevice 100 described with reference to FIG. 1, and can perform afunction performed by the AI device 100.

The term “water purifier” can be interchangeably used with the term “AIwater purifier”.

The processor can control an operation of the water purifier.

FIG. 6 is a view for explaining an operation method of an AI waterpurifier.

The operation method of the AI water purifier can include photographing(i.e., capturing) water passing through a water supply pipe (S610),acquiring a pollution level of the water passing through the watersupply pipe (S630), stopping the supply of the water passing through thewater supply pipe based on a pollution level (S650), and outputtingnotification of a water supply check (S670).

FIG. 7 is a view for explaining a water supply pipe and a supplied waterquality management module.

FIG. 8 is a plan view of a photography surface and a photography areaformed on an inner surface of a water supply pipe.

The supplied water quality management module can include a first camera760. Here, the first camera 760 can photograph (i.e., capture) waterpassing through the water supply pipe.

First, a shape of a water supply pipe 700 will be described.

The water supply pipe 700 can have a cylindrical shape and a partialsurface of the water supply pipe 700 can be transparent. A region of thewater supply pipe 700, which is formed to be transparent, can bereferred to as a transparent region 710. Thus, the inner surfaceopposing the transparent region 710 can be seen through the transparentregion 710.

The first camera 760 can be installed outside the water supply pipe. Thefirst camera 760 can be installed to see the inside of the water supplypipe 700 through the transparent region 710 in order to photograph thewater passing through the water supply pipe.

A photography surface 720 can be formed on an inner surface 741 of thewater supply pipe 700. Here, the photography surface 720 can be formedat a position opposing the transparent region 710.

When the first camera 760 is installed to see the inside of the watersupply pipe through the transparent region 710, the photography surface720 can be formed toward a photography direction of the first camera760. Thus, the photography surface 720 can be formed at a positionopposing the first camera 760.

Thus, the first camera 760 can be disposed outside the water supplypipe, and can perform photography to overlap a photography area 721,water passing through the photography area 721, and the transparentregion 710 with one another.

The inner surface 741 of the water supply pipe 700 can include thephotography area 721. Here, the photography area 721 can refer to aregion that is formed on the inner surface of the water supply pipe 700and is photographed by the first camera 760.

The photography area 721 can be formed on the photography surface 720.Thus, the photography area 721 can also be formed at a position opposingthe transparent region 710 and the first camera 760.

The photography surface 720 can be formed on the inner surface of thewater supply pipe 700, and the photography area 721 can be formed on thephotography surface 720, but the present disclosure is not limitedthereto. For example, without the separate photography surface 720, thephotography area 721 can be formed on a portion of the inner surface ofthe water supply pipe 700. However, in this case, the photography area721 can also be disposed to oppose the transparent region 710, and thephotography area 721 can also be disposed to oppose the first camera760.

The photography area 721 can be formed to be opaque. In detail, thephotography surface 720 can be formed of an opaque material, and thusthe photography area 721 can also be formed to be opaque.

When the photography surface 720 is not present, the inner surface ofthe water supply pipe 700, on which the photography area 721 is formed,can be formed of an opaque material.

The water supply pipe 700 can include a sensor hole 750. A water qualitysensor 770 can be inserted into the sensor hole 750, and the insertedwater quality sensor 770 can directly contact water to acquire data fordetermining water quality.

Referring to FIG. 8, the photography area 721 can include a mark 810.

In detail, the mark 810 can be formed on the photography surface 720,and when the photography surface 720 is not present, the mark 810 can bedirectly formed on the inner surface of the water supply pipe 700.

The mark 810 can include a numeral, a character, a cover, a sign, animage, a taste, or the like as a shape for indicating predeterminedinformation.

The photography area 721 can include a background region 820. Here, thebackground region can refer to a region of the photography area 721, onwhich the mark 810 is not formed. The background region 820 can havesingle color.

The background region 820 can be formed on the photography surface 720,but the present disclosure is not limited thereto, and when thephotography surface 720 is not present, the background region 820 can bedirectly formed on the inner surface of the water supply pipe 700.

The processor can control the first camera to photograph the waterpassing through the water supply pipe, and can determine a pollutionlevel of the water passing through the water supply pipe using an imagecaptured by photographing the water passing through the water supplypipe.

Information related thereto will be described with reference to FIGS. 9to 11.

FIGS. 9 to 11 are views for explaining a method of determining apollution level using an image captured by photographing water.

The processor can acquire at least one of transparency or color of waterpassing through a water supply pipe using the image captured byphotographing the water passing through the water supply pipe, and candetermine a pollution level of the water passing through the watersupply pipe using at least one of the transparency or the color.

First, a method of acquiring transparency of water will be describedwith reference to FIGS. 9 to 10.

The transparency of water can be determined based on at least one of thesharpness of water or impurities in water.

First, the sharpness of water will be described. Here, the term‘sharpness’ can be interchangeably used with the term ‘definition’.

Here, the sharpness can refer to a degree by which a subject or imagedisplayed on a monitor is clear. The sharpness can indicate a cleardegree at a boundary between dark and light of the image. The sharpnesscan refer to a degree by which the image is clear and light.

When water passing through the water supply pipe has good water quality,if the inside of the water supply pipe is seen through the transparentregion, an opposite wall needs to be clearly seen. For example, as shownin FIG. 9A, a mark 921 formed on the photography area can be clearlyseen in an image 910 captured in a state in which water quality of waterpassing through the water supply pipe is good.

In contrast, when water with large amount of impurities passes throughthe water supply pipe, an opposite wall may not be clearly seen and ablurry shape can be seen. For example, as shown in FIG. 9B, a mark 922formed on the photography area can be unclearly seen in an image 920captured in a state in which water quality of the water passing throughthe water supply pipe is not good.

Thus, the processor can determine the transparency of water passingthrough the water supply pipe using the sharpness of the image capturedby photographing the water passing through the water supply pipe.

In detail, when photography is performed using the first camera,photography can be performed to overlap the water passing through thephotography area and the mark 922 included in the photography area witheach other.

The processor can acquire the sharpness of the mark 922 using the imagecaptured to overlap the water passing through the photography area andthe mark 922 with each other.

In detail, the processor can acquire the sharpness of the mark 922 usingthe color of the mark 922, a clear degree at a boundary of the mark 922,gradation at a boundary of the mark 922, or the like.

The processor can determine the transparency of the water passingthrough the water supply pipe of the sharpness of the mark 922. Forexample, when sharpness is high, the transparency of water can also bedetermined to be high, and when the sharpness is low, the transparencyof water can also be determined to be low.

A phenomenon whereby a photography area is burred due to pollution ofwater can be similar to a phenomenon caused in the case of focusing-outof a camera. Thus, the processor can determine sharpness using a focusout detection algorithm of a camera.

Hereinafter, a method of determining transparence based on impurities inwater will be described.

When water quality of the water passing through the water supply pipe isgood, the amount of impurities in water is low. For example, as shown inFIG. 10A, impurities may not be detected and a small amount ofimpurities can be detected in an image 1010 captured in a state in whichwater quality of water passing through the water supply pipe is good.

In contrast, when water with a high pollution level passes through thewater supply pipe, a large amount of impurities is high. For example, asshown in FIG. 10B, impurities can be detected or a large amount ofimpurities 1021 can be detected in an image 1020 captured in a state inwhich water quality of water passing through the water supply pipe isnot good.

Thus, the processor can detect impurities using an image captured byphotographing water passing through the photography area and candetermine the transparency of the water passing through the water supplypipe based on the impurities.

In detail, the processor can detect impurities from a captured image andcan determine the amount of the detected impurities.

The processor can determine the transparency of the water passingthrough the water supply pipe based on the amount of impurities. Forexample, when a small amount of impurities is present, the transparencyof water can be determined to be high, and when a large amount ofimpurities is high, the transparency of water can be determined to below.

The processor can detect impurities in a background region of singlecolor, and can determine the transparency of water passing through thewater supply pipe based on the impurities. In this case, the backgroundregion can be white.

That is, when the background region has single color (in particular,when the background region is white), impurities can be more easilydetected, and thus, the processor can detect impurities in thebackground region of single color.

There is a camera occlusion detection algorithm of detecting a cameralens to be hidden by the hand or other objects through the capturedimage. A phenomenon caused when impurities are contained in water issimilar to a phenomenon whereby the camera lens is hidden. Thus, theprocessor can determine the amount of impurities in water using thecamera occlusion detection algorithm.

The processor can determine a pollution level of water using thetransparency of water. For example, when the transparency of water ishigh, a pollution level of water can be determined to be low, and whenthe transparency of water is low, the pollution level of water can bedetermined to be high.

The processor can determine a pollution level of the water passingthrough the water supply pipe using at least one of the transparency orcolor of water. The transparency of water has been described withreference to FIGS. 9 to 10, and thus a method of determining a pollutionlevel using the color of water will be described with reference to FIG.11.

Hereinafter, a method of determining a pollution level using color ofwater will be described with reference to FIG. 11.

When water quality of water passing through the water supply pipe isgood, water can have constant color. For example, as shown in FIG. 11A,a background region can represent color of the photography surface 720in an image 1110 captured in a state in which image quality of the waterpassing through the water supply pipe is good. When the photography areais directly formed in the inner surface of the water supply pipe, thebackground region can represent the color of the inner surface of thewater supply pipe.

In contrast, when water with a high pollution level passes through thewater supply pipe, the color of water can be changed. For example, asshown in FIG. 11B, a background region can represent different colorfrom the photography surface 720 in an image 1210 captured in a state inwhich water with a high pollution level, such as rust, passes. When thephotography area is directly formed on the inner surface of the watersupply pipe, the background region can represent difference color fromthe inner surface of the water supply pipe in the captured image 1210.

Thus, the processor can detect the color of an image using an imagecaptured by photographing water passing through the photography area.

The processor can determine a pollution level of water using a variationof color of an image.

For example, when the variation of the color of the image is higher thanin a normal state (when a pollution level of water is equal to or lessthan a reference value), the pollution level of water can be determinedto be high, and when the variation of the color of the image is lowerthan in the normal state, the pollution level of water can be determinedto be low.

The processor can detect color in the background region of single colorand can determine a pollution level of water based on the detectedcolor. In this case, the background region can be white.

That is, when the background region has single color (in particular,when the background region is white), a color variation is more easilydetected, and thus the processor can detect color in the backgroundregion of single color (in particular, white).

FIG. 12 is a view for explaining a method of giving a warning orstopping an operation depending on a pollution level of water passingthrough a water supply pipe.

The processor can stop water supply or can output notification of watersupply check depending on a pollution level of water passing through thewater supply pipe.

In detail, the processor can stop water supply or can outputnotification of water supply check when a rate of rise of a pollutionlevel of water passing through the water supply pipe is equal to orgreater than a preset value.

Here, the rate of rise of the pollution level of water can refer to anamount of rise per unit time (a) of the pollution level, that is, arising inclination of the pollution level. The preset value can refer toa reference inclination (b).

When an amount of rise per unit time (a) of a pollution level is equalto or greater than a preset value, the processor can stop water supplyor can output notification of water supply check.

The processor can control the water supply valve 61 to be closed, andthus can stop water supply from the water source to the filter assemblythrough the water supply pipe. As another method, the processor can stopan operation of a pump for circulating water, and thus can stop watersupply from the water source to the filter assembly through the watersupply pipe.

The processor can control an output interface to output notification ofwater supply check or can output notification of water supply checkusing a method of transmitting the notification of water supply check toa terminal of the user.

As such, according to the present disclosure, it can be advantageousthat water quality is directly measured by the water purifier. Inaddition, water quality is measured using the camera, and thusmanufacturing costs can be advantageously reduced.

According to the present disclosure, the water supply pipe measures apollution level and water supply to a filter array can be blocked whencontamination occurs, and thus a pollutant supplied to the filter can bepreviously blocked. Thus, according to the present disclosure, it can beadvantageous that the lifespan of the filter is largely reduced due tothe pollutant or non-filtering of the pollutant can be prevented.

The water purifier can be a device for filtering a pollutant, and thuswater supplied from the water source contains pollutant. With regard toa degree by which a pollution level slightly rises, the water purifiercan be capable of sufficiently purifying water. However, according tothe present disclosure, when a rate of rise of a pollution level isequal to or greater than a preset value (that is, when a large amount ofpollutant is rapidly input), supply of the pollutant is blocked, andthus an operation of the water purifier can be stopped only if necessary(when the lifetime of a filter needs to be prevented from being reducedor non-filtering of the pollutant needs to be prevented).

According to the present disclosure, the pollution level is detectedusing the marker and the background region, and thus the accuracy ofdetection can be advantageously enhanced.

According to the present disclosure, the water supply pipe can beconfigured to be partially transparent, and the camera is disposedoutside the water supply pipe, and thus, it can be possible to use acamera that is not waterproof, thereby reducing manufacturing costs.

According to the present disclosure, a pollution level can be detectedusing a pre-present algorithm (a focusing-out detection algorithm, or acamera occlusion detection algorithm), and thus manufacturing costs canbe advantageously reduced.

Pollution level detection can also be performed by a water qualitysensor as well as a camera.

In detail, the water purifier can include a water quality sensor foracquiring data for determining water quality.

The processor can determine the second pollution level of the waterpassing through the water supply pipe using the ingredient level inwater, acquired based on the data. A method of determining a pollutionlevel using an ingredient level acquired using the water quality sensoris a related art, and thus a detailed description thereof is notomitted.

When the rate of rise of the pollution level, acquired using the firstcamera, is equal to or greater than a preset value or the rate of riseof the second pollution level is equal to or greater than a secondpreset value, the processor can stop water supply through the watersupply pipe and can output notification of water supply check.

That is, according to the present disclosure, any one of the imageanalysis result and a sensing value of the water quality sensor isabnormal, it can be determined that contamination occurs, and thus theaccuracy of detection of contamination can be advantageously enhanced.In particular, both the image analysis result and the water qualitysensor are used, and thus the water quality sensor with relatively lowperformance can be used, thereby advantageously reducing manufacturingcosts.

FIG. 13 is a view for explaining a method of determining a pollutionsource.

The processor can determine a pollution source using the image capturedby photographing water passing through the water supply pipe and theingredient level in water, acquired based on the water quality sensor,and can output information indicating the pollution source.

In this case, the processor can determine the pollution source using apollution source detection model.

Here, a pollution source detection model 1310 can be a neural networktrained using data for training purposes, including an image captured byphotographing the water passing the water supply pipe and an ingredientlevel, and a pollution source for training purposes, labeled on the datafor training purposes.

In detail, the learning device 200 can train a neural network bylabeling a pollution source for training purposes on training dataincluding the image captured by photographing the water passing throughthe water supply pipe and the ingredient level.

In more detail, the learning device 200 can train a neural networkusing, as an input, an image (an image captured by photographingwatering passing through the water supply pipe for training purposes)for training purposes and an ingredient level (an ingredient leveldetected by a water quality sensor in a state in which the image fortraining purposes is captured) for training purposes, and using, as anoutput value, a pollution source (a pollutant contained in water in asituation in which the image for training purposes, e.g., rust) fortraining purposes.

Here, the pollution source for training purposes can be an answer to beinferred using the image for training purposes and the ingredient levelfor training purposes by the neural network.

In this case, the neural network can infer a function of a correlationbetween labeling data and data for training purposes using the labelingdata and the data for training purposes. Through evaluation of thefunction inferred by the neural network, a parameter (a weight, bias, orthe like) of the neural network can be determined (optimized).

The neural network trained using the above method can be referred to asthe pollution source detection model 1310.

The pollution source detection model 1310 can be installed in the waterpurifier.

In detail, the pollution source detection model 1310 can be embodied inhardware, software, or a combination of hardware and software. When thepollution source detection model 1310 is entirely or partially embodiedin software, one or more commands configuring an AI model can be storedin a memory of the water purifier.

The processor 180 can provide the image captured by photographing waterpassing through the water supply pipe and the ingredient level acquiredby the water quality sensor to the pollution source detection model1310, and thus can acquire the pollution source output by the pollutionsource detection model.

In detail, the pollution source detection model 1310 can extract afeature vector from the image captured by photographing the waterpassing through the water supply pipe and the ingredient level acquiredby the water quality sensor, based on the set parameter, and can outputa pollution source corresponding to input data based on the featurevector.

When acquiring the pollution source, the processor 180 can outputinformation indicating the pollution source. For example, the processorcan control a speaker to output a voice message “Rust is detected”.

The aforementioned structure and algorithm can also be applied to thewater discharge pipe. Hereinafter, the structure and operation of thewater discharge pipe will be described in terms of a difference fromthose of the water supply pipe.

A discharged water quality management module can include a secondcamera. Here, the second camera can photograph water passing through thewater discharge pipe.

The processor can acquire at least one of the transparency and color ofthe water passing through the water discharge pipe using the imagecaptured by photographing the water passing through the water dischargepipe, and can determine a pollution level of the water passing throughthe water discharge pipe using at least one of the transparency or thecolor.

FIG. 14 is a view for explaining a method of stopping water discharge ina water discharge pipe or outputting notification of discharged watercheck.

The processor can stop water discharge or can output notification ofdischarged water check based on the pollution level of the water passingthrough the water discharge pipe.

In detail, when the pollution level of the water passing through thewater discharge pipe is equal to or greater than a preset value, theprocessor can stop water discharge or can output discharged water check.

As described above, water supply can be stopped or notification of watersupply check can be output based on the rate of rise of a pollutionlevel of water in the water discharge pipe. This is needed to preventthe lifetime of a filter or to prevent non-filtering of a pollutantbecause a large amount of pollutant is remarkably injected.

However, the objective of the water discharge pipe can be not to providecontaminated water to a user. Thus, when the pollution level of waterpassing through the water discharge pipe is equal to or greater than apreset value, the processor can stop water discharge or can outputnotification of discharged water check.

The processor can control the ejection valve 67 to be closed, and thuswater discharge through the water outlet can be stopped. As anothermethod, the processor can stop an operation of a pump for circulatingwater, and thus the water can be prevented from being discharged throughthe water outlet.

The processor can control the an output interface to output notificationof water discharge check or can output notification of water dischargecheck using a method of transmitting the notification of water dischargecheck to a terminal of the user.

Hereinafter, a method of determining a pollution level of an AI waterpurifier will be described. The method of determining the pollutionlevel of the AI water purifier can include photographing water passingthrough a water supply pipe configured to connect a water source and afilter assembly, acquiring at least one of transparency or color of thewater passing through the water supply pipe using an image captured byphotographing the water passing through the water supply pipe, anddetermining a pollution level of the water passing through the watersupply pipe using at least one of the transparency or the color.

In this case, the method can further include stopping supply of waterthrough the water supply pipe when a rate of rise of the pollution levelis equal to or greater than a preset value.

The water supply pipe can include a photography area photographed by thefirst camera.

In this case, the photography area can include a mark, and the acquiringat least one of the transparency or the color of the water can includeacquiring sharpness of the mark using an image captured to overlap waterpassing through the photography area with the mark and determining thetransparency using the sharpness of the mark.

The acquiring at least one of the transparency or color of the water caninclude detecting impurities using an image captured by photographingthe water passing through the photography area and determining thetransparency based on the impurities.

The water supply pipe can include a transparent region formed to opposethe photography area and to be transparent, and the photographing thewater passing through the water supply pipe can include performingphotography to overlap the photography area, the water passing thephotography area, and the transparent region with one another using afirst camera disposed outside the water supply pipe.

The method of determining the pollution level of the AI water purifiercan further include acquiring an ingredient level in water based datacollected by a water quality sensor, determining a second pollutionlevel of the water passing through the water supply pipe using theingredient level in the water, and stopping supply of water through thewater supply pipe when the rate of rise of the pollution level is equalto or greater than a preset value or a rate of rise of the secondpollution level is equal to or greater than a second preset value.

In this case, the method can further include determining a pollutionsource using the image captured by photographing the water passingthrough the water supply pipe and the ingredient level, and outputtinginformation indicating the pollution source.

According to the present disclosure, it can be advantageous that thewater purifier advantageously measures water quality. In addition, waterquality is measured using the camera, and thus manufacturing costs canbe advantageously reduced.

According to the present disclosure, the water supply pipe measures apollution level and water supply to a filter array can be blocked whencontamination occurs, and thus a pollutant supplied to the filter can bepreviously blocked. Thus, according to the present disclosure, it can beadvantageous that the lifespan of the filter is largely reduced due tothe pollutant or non-filtering of the pollutant can be prevented.

The above-described present disclosure can be implemented as acomputer-readable code on a computer-readable medium in which a programis stored. The computer readable recording medium includes all types ofrecording devices in which data readable by a computer system is stored.Examples of the computer-readable recording medium include hard diskdrives (HDD), solid state disks (SSD), silicon disk drives (SDD), readonly memories (ROMs), random access memories (RAMS), compact disc readonly memories (CD-ROMs), magnetic tapes, floppy discs, and optical datastorage devices. Also, the computer can include a control unit 180 ofthe terminal.

Therefore, the detailed description is intended to be illustrative, butnot limiting in all aspects. It is intended that the scope of thepresent disclosure should be determined by the rational interpretationof the claims as set forth, and the modifications and variations of thepresent disclosure come within the scope of the appended claims andtheir equivalents.

What is claimed is:
 1. An artificial intelligence (AI) water purifier,comprising: a housing forming an outer surface of the AI water purifier;a filter assembly disposed inside the housing; a water outlet configuredto discharge water; a water supply pipe configured to connect a watersource to the filter assembly; a water discharge pipe configured toconnect the filter assembly to the water outlet; a camera configured tocapture an image of water passing through the water supply pipe; and aprocessor configured to: acquire at least one of transparency of thewater or color of the water passing through the water supply pipe usingthe captured image, and determine a first pollution level of the waterpassing through the water supply pipe using the at least one of thetransparency of the water or the color of the water.
 2. The AI waterpurifier of claim 1, further comprising a water supply valve, whereinthe processor is further configured to control the water supply valve tostop supply of the water through the water supply pipe when a rate ofincrease of the first pollution level is equal to or greater than apreset value.
 3. The AI water purifier of claim 1, wherein an innersurface of the water supply pipe includes a photography area, andwherein the captured image includes the photography area.
 4. The AIwater purifier of claim 3, wherein the photography area includes a mark,and wherein the processor is further configured to: acquire sharpness ofthe mark using the captured image, wherein the captured image includesthe water passing through the water supply pipe overlapping the mark,and determine the transparency of the water using the sharpness of themark.
 5. The AI water purifier of claim 3, wherein the processor isfurther configured to: detect impurities in the water using the capturedimage, the captured image including the water passing through thephotography area, and determine the transparency of the water based onthe detected impurities in the water.
 6. The AI water purifier of claim3, wherein the photography area includes a background region of a singlecolor, and wherein the processor is further configured to detect thecolor of the water in the background region.
 7. The AI water purifier ofclaim 3, wherein the water supply pipe includes a transparent regionopposite to the photography area, wherein the transparent region istransparent, and wherein the camera is disposed outside the water supplypipe and is disposed overlapping the transparent region of the watersupply pipe, and wherein the captured image includes the transparentregion overlapping the water passing through the water supply pipe andoverlapping the photography area.
 8. The AI water purifier of claim 2,further comprising: a water quality sensor configured to acquire datafor determining water quality of the water passing through the watersupply pipe, wherein the processor is further configured to: determine asecond pollution level of the water passing through the water supplypipe using an ingredient level of the water, acquired based on the datafrom the water quality sensor, and stop supply of water through thewater supply pipe, by controlling the water supply valve, when the rateof increase of the first pollution level is equal to or greater than thepreset value or a rate of increase of the second pollution level isequal to or greater than a second preset value.
 9. The AI water purifierof claim 8, wherein the processor is further configured to: acquire apollution source using the captured image and the ingredient level ofthe water, and output information indicating the pollution source. 10.The AI water purifier of claim 9, wherein the processor acquires thepollution source by transmitting the captured image and the ingredientlevel of the water to a pollution source detection model and receivingthe pollution source from the pollution source detection model, andwherein the pollution source detection model is a neural network trainedusing training data including at least a second image for trainingpurposes, acquired by capturing water passing through the water supplypipe, an ingredient level for training purposes, and a pollution sourcefor training purposes.
 11. An artificial intelligence (AI) waterpurifier, comprising: a housing forming an outer surface of the waterpurifier; a water outlet configured to discharge water; a water supplypipe configured to be connected to a water source, the water supply pipeincluding a transparent region that is transparent; a water dischargepipe connected to the water outlet; a camera overlapping the transparentregion of the water supply pipe, the camera being configured to captureat least one image of the transparent region overlapping water passingthrough the water supply pipe; and a processor configured to determine apollution level of the water passing through the water supply pipe usingthe at least one captured image and a neural network.
 12. The AI waterpurifier of claim 11, wherein the neural network is a pollution sourcedetection model that is trained using training data, the training dataincluding at least a second image for training purposes, acquired bycapturing water passing through the water supply pipe, an ingredientlevel for training purposes, and a pollution source for trainingpurposes.
 13. A method of determining a pollution level of an artificialintelligence (AI) water purifier, the method comprising: providing theAI water purifier, the AI water purifier including: a housing includinga filter assembly; a water supply pipe connecting a water source to thefilter assembly; and a camera; capturing by the camera, an image ofwater passing through the water supply pipe; acquiring at least one oftransparency of the water or color of the water passing through thewater supply pipe using the captured image; and determining a firstpollution level of the water passing through the water supply pipe usingthe at least one of the transparency of the water or the color of thewater.
 14. The method of claim 13, wherein the AI water purifier furtherincludes a water supply valve, and wherein the method further comprisesstopping supply of water through the water supply pipe, by controllingthe water supply valve, when a rate of increase of the first pollutionlevel is equal to or greater than a preset value.
 15. The method ofclaim 13, wherein an inner surface of the water supply pipe includes aphotography area, and wherein the captured image includes thephotography area.
 16. The method of claim 15, wherein the photographyarea includes a mark, and wherein the acquiring at least one of thetransparency of the water or the color of the water includes: acquiringsharpness of the mark using the captured image, wherein the capturedimage includes the water overlapping the mark; and determining thetransparency using the sharpness of the mark.
 17. The method of claim15, wherein the acquiring at least one of the transparency of the wateror the color of the water includes: detecting impurities in the waterusing the captured image, the captured image including the water passingthrough the photography area; and determining the transparency based onthe impurities in the water.
 18. The method of claim 15, wherein thewater supply pipe includes a transparent region opposite to thephotography area, wherein the transparent region is transparent, whereinthe camera is disposed outside the water supply pipe overlapping thetransparent region of the water supply pipe, and wherein the capturedimage includes the transparent region overlapping the water passingthrough the water supply pipe and overlapping the photography area. 19.The method of claim 14, wherein the AI water purifier further comprisesa water quality sensor, and wherein the method further comprises:acquiring an ingredient level of the water based data collected by thewater quality sensor; determining a second pollution level of the waterpassing through the water supply pipe using the ingredient level in thewater; and stopping supply of water through the water supply pipe, bythe controlling the water supply valve, when the rate of increase of thepollution level is equal to or greater than the preset value or a rateof increase of the second pollution level is equal to or greater than asecond preset value.
 20. The method of claim 19, further comprising:determining a pollution source using the captured image and theingredient level; and outputting information indicating the pollutionsource.